Design And Implementation of a Machinability and Damage Control Module for Go-Cf Reinforced Nanocomposites

This study presents the design and implementation of a machinability evaluation and damage control module for polymer nanocomposites reinforced with graphene oxide (GO) and carbon fiber (CF). The primary aim is to enhance the mechanical properties and optimize the machining performance of hybrid nanocomposites for advanced industrial applications. Nanocomposites were fabricated with varying concentrations of GO (0.1% to 0.5% by weight) within a CF-reinforced polymer matrix using a hand layup method. Mechanical characterization demonstrated that the composite with 0.3 wt% GO exhibited the highest tensile strength (96.5 MPa), flexural strength (135.4 MPa), and impact resistance (8.7 kJ/m2), highlighting the effectiveness of hybrid reinforcement. Machinability studies were conducted under different spindle speeds and feed rates. Surface roughness, delamination factor, and tool wear were evaluated using Taguchi-based L9 orthogonal arrays. The developed damage control module employed a hybrid CoCoSo-GRA optimization technique, validated through ANOVA, to identify optimal machining parameters. SEM analysis confirmed improved interfacial bonding and minimal machining-induced defects under optimized conditions. The proposed module successfully predicted machining behavior and minimized defects, offering a strategic tool for sustainable manufacturing. The integration of GO and CF not only enhanced material performance but also improved the machinability index, demonstrating the material’s suitability for aerospace, automotive, and electronics industries. This research advances the understanding of hybrid nanocomposites and provides a scalable framework for intelligent machining process control. Keywords: Graphene oxide (GO); Carbon fiber (CF); Polymer nanocomposites; Machinability; Surface roughness